Residence choices of Hispanic neighborhoods in Nevada

A well-known characteristic of Hispanic in the U.S. is their tendency to concentrate their settlement area in distinct locations. This study merges aggregate data of localities with micro observations to estimate the joint decisions of residential location and homeownership choice for Hispanics. To address the possibility that the disturbances in the regression may be correlated within groups, we apply a bivarite probit framework clustered by localities (PUMAs), using the Public Use Micro Statistics of Census 2000 data for Nevada. The results suggests that Hispanics choose to live in Hispanic enclaves are characterized by lower income, less English fluency, lower educational attainments and recent migration. Assessing the interaction of the homeownership decisions and location choice, we find that these two residential decisions are simultaneously determined and they have significantly positive effects on each other

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

Variations in developmental patterns across pragmatic features

Drawing on the findings of longitudinal studies in uninstructed contexts over the last two decades, this synthesis explores variations in developmental patterns across second language (L2) pragmatic features. Two synthesis questions were addressed: (a) What are the variations in developmental patterns across pragmatic features?, and (b) What are the potential explanations for the variations? In response to the first question, previous studies showed that L2 pragmatic development is a non-linear, dynamic process, with developmental paces varying across pragmatic features (Ortactepe, 2013; Taguchi, 2010, 2011, 2012; Warga & Scholmberger, 2007). These studies revealed that some aspects of pragmatic features (e.g., semantic strategies of speech acts) develop faster than others (e.g., lexical features such as mitigators). In response to the second question, three potential explanations were identified to account for the developmental variations: (a) language-related, (b) situation-dependent, and (c) learner-related explanations, with three subcategories for the language-related explanation: (a) the functions of pragmatic features, (b) the frequency of availability of target features, and (c) the similarity and difference between languages with respect to the target feature

Large scale modeling, model reduction and control design for a real-time mechatronic system

Mechatronics is the synergistic integration of the techniques from mechanical engineering, electrical engineering and information technology, which influences each other mutually. As a multidisciplinary domain, mechatronics is more than mechanical or electronics, and the mechatronic systems are always composed of a number of subsystems with various controllers. From this point of view, a lot of such systems can be defined as large scale system. The key element of such systems is integration. Modeling of mechatronic system is a very important step in developing control design of such products, so as to simulate and analyze their dynamic responses for control design, making sure they would meet the desired requirements. The models of large scale systems are always resulted in complex form and high in dimension, making the computation for modeling, simulation and control design become very complicated, or even beyond the solutions provided by conventional engineering methods. Therefore, a simplified model obtained by using model order reduction technique, which can preserve the dominant physical parameters and reveal the performance limiting factor, is preferred. In this dissertation, the research have chosen the two-wheeled self-balancing scooter as the subject of the study in research on large scale mechatronic system, and efforts have been put on developing a completed mathematical modeling method based on a unified framework from varitional method for both mechanical subsystem and electrical subsystem in the scooter. In order to decrease the computation efforts in simulation and control design, Routh model reduction technique was chosen from various model reduction techniques so as to obtain a low dimensional model. Matlab simulation is used to predict the system response based on the simplified model and related control design. Furthermore, the final design parameters were applied in the physical system of two-wheeled self-balancing scooter to test the real performance so as to finish the design evaluation. Conclusion was made based on these results and further research directions can be predicte